2021 Information Theory Society Paper Award Recipients Named
2021 Information Theory Society Paper Award recipients named
Jul 13, 2021

The Information Theory Society Paper Award is given annually for an outstanding publication in the fields of interest to the Society appearing anywhere during the preceding four calendar years.

The 2021 ITSoc Paper Award was decided by the Board of Governors during their Annual Meeting in June and announced during the ISIT 2021 awards ceremony. The award will be formally presented at ISIT 2022 next year.

The recipients of this year's award are:
Shrinivas Kudekar; Santhosh Kumar; Marco Mondelli; Henry D. Pfister; Eren Şaşoǧlu; Rüdiger L. Urbanke, “Reed–Muller Codes Achieve Capacity on Erasure Channels,” in IEEE Transactions on Information Theory, vol. 63, no. 7, pp. 4298-4316, July 2017.


We introduce a new approach to proving that a sequence of deterministic linear codes achieves capacity on an erasure channel under maximum a posteriori decoding. Rather than relying on the precise structure of the codes, our method exploits code symmetry. In particular, the technique applies to any sequence of linear codes where the blocklengths are strictly increasing, the code rates converge, and the permutation group of each code is doubly transitive. In other words, we show that symmetry alone implies near-optimal performance. An important consequence of this result is that a sequence of Reed-Muller codes with increasing block length and converging rate achieves capacity. This possibility has been suggested previously in the literature but it has only been proven for cases where the limiting code rate is 0 or 1. Moreover, these results extend naturally to all affine-invariant codes and, thus, to extended primitive narrow-sense BCH codes. This also resolves, in the affirmative, the existence question for capacity-achieving sequences of binary cyclic codes. The primary tools used in the proof are the sharp threshold property for symmetric monotone Boolean functions and the area theorem for extrinsic information transfer functions.